Researchers from the Indian Institute of Technology Madras (IIT Madras) and Christian Medical College (CMC), Vellore, have developed three AI-based tools to support the early detection and assessment of kidney diseases.

The technologies include a machine learning model that predicts the risk of chronic kidney disease (CKD) using clinical and laboratory data; a deep learning system that analyses CT scans and classifies kidneys as normal or affected by cysts, stones or tumours; and a 3D imaging platform that reconstructs kidneys from CT scans to measure tumour volume and the percentage of kidney involvement.

The CT classifier was trained on more than 12,000 images. The 3D imaging framework uses open-source software and is designed to provide repeatable measurements of tumour burden. “The team aimed to develop intelligent systems that would help clinicians make quicker and more informed decisions. We used machine learning along with clinical knowledge to develop tools that would assist in the earlier detection of kidney diseases and give more detailed information specific to the patient,” said Prof. G.L. Samuel, Department of Mechanical Engineering, IIT Madras.

The CKD prediction model has also been implemented as a prototype interface to support future clinical use. The researchers worked on improving its accuracy and interpretability for doctors.

“Early detection is of paramount importance when dealing with kidney diseases; these AI tools can help detect at-risk patients early and plan their treatment more effectively. The patient-specific imaging framework is of significant promise as it goes beyond the standard measurements to give a more comprehensive picture of the extent of the disease,” said Jennifer Delighta, research scholar, IIT Madras.

The research was led by Prof. G.L. Samuel and Jennifer Delighta in collaboration with Prof. Santosh Varughese, Department of Nephrology, CMC Vellore. It received institutional support from IIT Madras and the SPARC project. The team plans to validate the models using additional patient datasets and work with healthcare institutions on real-world deployment. Researchers are also exploring their integration with wearable sensing systems and kidney Digital Twin platforms for personalised monitoring and treatment planning.

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